Abstract

The accelerating global transition toward renewable energy sources, driven by decarbonization imperatives and technological maturation, has fundamentally altered the operational paradigm of electrical power grids. Solar photovoltaic and wind generation—collectively constituting the predominant share of new renewable capacity—introduce unprecedented challenges through their inherent temporal variability, spatial distribution, and partial unpredictability. Conventional deterministic grid operation models, predicated on dispatchable centralized generation, prove inadequate for systems characterized by bidirectional power flows, distributed energy resources, and stochastic supply profiles. This review presents a comprehensive engineering and computational examination of modeling, simulation, and optimization frameworks enabling high-penetration renewable integration within smart grid infrastructures. We systematically analyze foundational engineering models for solar PV arrays and wind turbine generators, computational simulation methodologies including stochastic optimal power flow and dynamic stability assessment, AI-based forecasting architectures utilizing hybrid deep learning approaches, and smart grid control frameworks encompassing distributed energy resource management systems and digital twin-enabled grid simulation. Through critical evaluation of translational deployment cases—including utility-scale solar-wind hybrid systems, urban microgrid implementations, and grid congestion mitigation pilots—we assess evidenced operational outcomes: forecasting accuracy improvements exceeding 25%, renewable curtailment reduction of 18-35%, and frequency regulation enhancement under 80% instantaneous penetration scenarios. Persistent challenges including computational scalability of real-time optimization, cybersecurity vulnerabilities in cyber-physical energy systems, and interoperability constraints with legacy infrastructure are systematically analyzed. Future trajectories emphasize edge-native decentralized intelligence, autonomous grid self-healing architectures, and physics-informed neural networks bridging high-fidelity simulation with operational deployment. This review provides power systems engineers, computational researchers, and grid modernization practitioners with an integrated methodological framework for engineering the renewable-intensive, resilient, and intelligent smart grids of the coming decade.